OpenRaiser/Intern-Atlas huggingface dataset:
Inverting normative city theories and computational urban models: Towards a coexistence with urban data streams
Venue: arXiv cs.CY 2017
Year: 2017

Two unavoidable processes punctuate our century: The unprecedented urbanisation of our planet (United Nations, 2014) and the spread of ubiquitous computing (Weiser, 1991) and urban data streams. This process of urbanisation corresponds with the process of digitalisation of urban life: while urbanisation acts on a physical infrastructural level, the digital develops as a kind of metastructure above the infrastructure. This metastructural level offers a flexible framework through which information is continuously and operatively being symbolized. Today, Information technology and the availability of abundant urban data streams could be considered as forerunners of our time, having unprecedented impacts comparable to the ones brought by the steam engine at the dawn of industrialisation and the electrification of cities. It is therefore no longer conceivable to think of the physical structure of the city without including its digital counterpart. Against this background, we will explore the role of computational power and information technologies as dominant factors in the formation of computational urban models and normative city theories. We will show how these models and theories, emerging mainly during the 19th and 20th centuries, present leaping correspondences with more ancient conceptions of the city, when observed from a meta-level or episteme (Foucault, 2002) approach. First, and for the sake of clarity, we will deal with some methodological elucidations around the concepts of theory, model and episteme, and how we will refer conceptually to these terms throughout this paper. Secondly, we will review these evolving technological and computational levels of abstraction and their influence on the different conceptions of the city. Thirdly, we will develop the hypothesis of a conceptual gap, between our current technological capacity -- grounded on the abundance and availability of urban data streams -- and the state of the art in urban modelling and city theory. Lastly, building upon Foucault's concept of episteme (Foucault, 1970) and genealogy (Foucault, 1977b), we will explore this gap by speculating around the possibility of an inversion in computational urban modelling and city theory. And above all, we will question the terms in which we can think of the city, in an age where the world can be virtually conceived as fully urban, and the continuity and abundance of urban data streams giving account of it can be taken for granted. How are we articulating the phenomena we call city on top of this generic common ground?
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Venue: arXiv cs.CV 2017
Year: 2017

As a generalization of the two-dimensional Fourier transform (2D FT) and 2D fractional Fourier transform, the 2D nonseparable linear canonical transform (2D NsLCT) is useful in optics, signal and image processing. To reduce the digital implementation complexity of the 2D NsLCT, some previous works decomposed the 2D NsLCT into several low-complexity operations, including 2D FT, 2D chirp multiplication (2D CM) and 2D affine transformations. However, 2D affine transformations will introduce interpolation error. In this paper, we propose a new decomposition called CM-CC-CM-CC decomposition, which decomposes the 2D NsLCT into two 2D CMs and two 2D chirp convolutions (2D CCs). No 2D affine transforms are involved. Simulation results show that the proposed methods have higher accuracy, lower computational complexity and smaller error in the additivity property compared with the previous works. Plus, the proposed methods have perfect reversibility property that one can reconstruct the input signal/image losslessly from the output.
Proactive Defense Against Physical Denial of Service Attacks using Poisson Signaling Games
Venue: arXiv cs.CR 2017
Year: 2017

While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks. In particular, distributed denial-of-service (DDoS) attacks overload the bandwidth of a server. But many IoT devices form part of cyber-physical systems (CPS). Therefore, they can be used to launch "physical" denial-of-service attacks (PDoS) in which IoT devices overflow the "physical bandwidth" of a CPS. In this paper, we quantify the population-based risk to a group of IoT devices targeted by malware for a PDoS attack. In order to model the recruitment of bots, we develop a "Poisson signaling game," a signaling game with an unknown number of receivers, which have varying abilities to detect deception. Then we use a version of this game to analyze two mechanisms (legal and economic) to deter botnet recruitment. Equilibrium results indicate that 1) defenders can bound botnet activity, and 2) legislating a minimum level of security has only a limited effect, while incentivizing active defense can decrease botnet activity arbitrarily. This work provides a quantitative foundation for proactive PDoS defense.
The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation
Venue: arXiv cs.CL 2017
Year: 2017

Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics. Preserving the anonymity of a text's writer can be also important in some other contexts, e.g., in the case of witness protection or anonymity programs. However, each person has his/her own style of writing, which can be analyzed using stylometry, and as a result, the true identity of the author of a piece of text can be revealed even if s/he has tried to hide it. Thus, it could be helpful to design automatic tools that can help a person obfuscate his/her identity when writing text. In particular, here we propose an approach that changes the text, so that it is pushed towards average values for some general stylometric characteristics, thus making the use of these characteristics less discriminative. The approach consists of three main steps: first, we calculate the values for some popular stylometric metrics that can indicate authorship; then we apply various transformations to the text, so that these metrics are adjusted towards the average level, while preserving the semantics and the soundness of the text; and finally, we add random noise. This approach turned out to be very efficient, and yielded the best performance on the Author Obfuscation task at the PAN-2016 competition.
Conflict Analysis for Pythagorean Fuzzy Information Systems with Group Decision Making
Venue: arXiv cs.AI 2017
Year: 2017

Pythagorean fuzzy sets provide stronger ability than intuitionistic fuzzy sets to model uncertainty information and knowledge, but little effort has been paid to conflict analysis of Pythagorean fuzzy information systems. In this paper, we present three types of positive, central, and negative alliances with different thresholds, and employ examples to illustrate how to construct the positive, central, and negative alliances. Then we study conflict analysis of Pythagorean fuzzy information systems based on Bayesian minimum risk theory. Finally, we investigate group conflict analysis of Pythagorean fuzzy information systems based on Bayesian minimum risk theory.
Large-scale Multiview 3D Hand Pose Dataset
Venue: arXiv cs.HC 2017
Year: 2017

Accurate hand pose estimation at joint level has several uses on human-robot interaction, user interfacing and virtual reality applications. Yet, it currently is not a solved problem. The novel deep learning techniques could make a great improvement on this matter but they need a huge amount of annotated data. The hand pose datasets released so far present some issues that make them impossible to use on deep learning methods such as the few number of samples, high-level abstraction annotations or samples consisting in depth maps. In this work, we introduce a multiview hand pose dataset in which we provide color images of hands and different kind of annotations for each, i.e the bounding box and the 2D and 3D location on the joints in the hand. Besides, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation.
Revisiting Elementary Denotational Semantics
Venue: arXiv cs.PL 2017
Year: 2017

Operational semantics have been enormously successful, in large part due to its flexibility and simplicity, but they are not compositional. Denotational semantics, on the other hand, are compositional but the lattice-theoretic models are complex and difficult to scale to large languages. However, there are elementary models of the $λ$-calculus that are much less complex: by Coppo, Dezani-Ciancaglini, and Salle (1979), Engeler (1981), and Plotkin (1993). This paper takes first steps toward answering the question: can elementary models be good for the day-to-day work of language specification, mechanization, and compiler correctness? The elementary models in the literature are simple, but they are not as intuitive as they could be. To remedy this, we create a new model that represents functions literally as finite graphs. Regarding mechanization, we give the first machine-checked proof of soundness and completeness of an elementary model with respect to an operational semantics. Regarding compiler correctness, we define a polyvariant inliner for the call-by-value $λ$-calculus and prove that its output is contextually equivalent to its input. Toward scaling elementary models to larger languages, we formulate our semantics in a monadic style, give a semantics for System F with general recursion, and mechanize the proof of type soundness.
Optimised finite difference computation from symbolic equations
Venue: arXiv cs.MS 2017
Year: 2017

Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.
Representation Learning for Grounded Spatial Reasoning
Venue: arXiv cs.CL 2017
Year: 2017

The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.
Human Sexual Cycles are Driven by Culture and Match Collective Moods
Venue: arXiv cs.SI 2017
Year: 2017

It is a long-standing question whether human sexual and reproductive cycles are affected predominantly by biology or culture. The literature is mixed with respect to whether biological or cultural factors best explain the reproduction cycle phenomenon, with biological explanations dominating the argument. The biological hypothesis proposes that human reproductive cycles are an adaptation to the seasonal cycles caused by hemisphere positioning, while the cultural hypothesis proposes that conception dates vary mostly due to cultural factors, such as vacation schedule or religious holidays. However, for many countries, common records used to investigate these hypotheses are incomplete or unavailable, biasing existing analysis towards primarily Christian countries in the Northern Hemisphere. Here we show that interest in sex peaks sharply online during major cultural and religious celebrations, regardless of hemisphere location. This online interest, when shifted by nine months, corresponds to documented human birth cycles, even after adjusting for numerous factors such as language, season, and amount of free time due to holidays. We further show that mood, measured independently on Twitter, contains distinct collective emotions associated with those cultural celebrations, and these collective moods correlate with sex search volume outside of these holidays as well. Our results provide converging evidence that the cyclic sexual and reproductive behavior of human populations is mostly driven by culture and that this interest in sex is associated with specific emotions, characteristic of, but not limited to, major cultural and religious celebrations.
Predicting Causes of Reformulation in Intelligent Assistants
Venue: arXiv cs.CL 2017
Year: 2017

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Venue: arXiv cs.AI 2017
Year: 2017

Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning. In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data. We show that sample complexity reduction through learning structure is possible for at least two simple cases. In studying these cases, we also gain insight into how this might be done for more complex domains.
Learning Photography Aesthetics with Deep CNNs
Venue: arXiv cs.CV 2017
Year: 2017

Automatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eight aesthetic attributes along with the overall aesthetic score. We report near human performance in the prediction of the overall aesthetic score. To understand the internal representation of these attributes in the learned model, we also develop the visualization technique using back propagation of gradients. These visualizations highlight the important image regions for the corresponding attributes, thus providing insights about model's representation of these attributes. We showcase the diversity and complexity associated with different attributes through a qualitative analysis of the activation maps.
Correction to "The Generalized Stochastic Likelihood Decoder: Random Coding and Expurgated Bounds"
Venue: arXiv cs.IT 2017
Year: 2017

This purpose of this letter is to handle a gap that was found in the proof of Theorem 2 in the paper "The generalized stochastic likelihood decoder: random coding and expurgated bounds."
Back-to-back Converter Control of Grid-connected Wind Turbine to Mitigate Voltage Drop Caused by Faults
Venue: arXiv cs.SY 2017
Year: 2017

Power electronic converters enable wind turbines, operating at variable speed, to generate electricity more efficiently. Among variable speed operating turbine generators, permanent magnetic synchronous generator (PMSG) has got more attentions due to low cost and maintenance requirements. In addition, the converter in a wind turbine with PMSG decouples the turbine from the power grid, which favors them for grid codes. In this paper, the performance of back-to-back (B2B) converter control of a wind turbine system with PMSG is investigated on a faulty grid. The switching strategy of the grid side converter is designed to improve voltage drop caused by the fault in the grid while the maximum available active power of wind turbine system is injected to the grid and the DC link voltage in the converter is regulated. The methodology of the converter control is elaborated in details and its performance on a sample faulty grid is assessed through simulation.
An Earthworm-Inspired Soft Crawling Robot Controlled by Friction
Venue: arXiv cs.RO 2017
Year: 2017

We present the modeling, design, fabrication and feedback control of an earthworm-inspired soft robot capable of crawling on surfaces by actively manipulating the frictional force between its body and the surface. Earthworms are segmented worms composed of repeating units known as metameres. The muscle and setae structure embedded in each individual metamere makes possible its peristaltic locomotion both under and above ground. Here, we propose a pneumatically-driven soft robotic system made of parts analogous to the muscle and setae structure and can replicate the crawling motion of a single earthworm metamere. A model is also introduced to describe the crawling dynamics of the proposed robotic system and proven be controllable. Robust crawling locomotion is then experimentally verified.
PAWS: A Tool for the Analysis of Weighted Systems
Venue: arXiv cs.FL 2017
Year: 2017

PAWS is a tool to analyse the behaviour of weighted automata and conditional transition systems. At its core PAWS is based on a generic implementation of algorithms for checking language equivalence in weighted automata and bisimulation in conditional transition systems. This architecture allows for the use of arbitrary user-defined semirings. New semirings can be generated during run-time and the user can rely on numerous automatisation techniques to create new semiring structures for PAWS' algorithms. Basic semirings such as distributive complete lattices and fields of fractions can be defined by specifying few parameters, more exotic semirings can be generated from other semirings or defined from scratch using a built-in semiring generator. In the most general case, users can define new semirings by programming (in C#) the base operations of the semiring and a procedure to solve linear equations and use their newly generated semiring in the analysis tools that PAWS offers.
UTS submission to Google YouTube-8M Challenge 2017
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present our solution to Google YouTube-8M Video Classification Challenge 2017. We leveraged both video-level and frame-level features in the submission. For video-level classification, we simply used a 200-mixture Mixture of Experts (MoE) layer, which achieves GAP 0.802 on the validation set with a single model. For frame-level classification, we utilized several variants of recurrent neural networks, sequence aggregation with attention mechanism and 1D convolutional models. We achieved GAP 0.8408 on the private testing set with the ensemble model. The source code of our models can be found in \url{https://github.com/ffmpbgrnn/yt8m}.
On Repair with Probabilistic Attribute Grammars
Venue: arXiv cs.PL 2017
Year: 2017

Program synthesis and repair have emerged as an exciting area of research, driven by the potential for revolutionary advances in programmer productivity. Among most promising ideas emerging for synthesis are syntax-driven search, probabilistic models of code, and the use of input-output examples. Our paper shows how to combine these techniques and use them for program repair, which is among the most relevant applications of synthesis to general-purpose code. Our approach combines semantic specifications, in the form of pre- and post-conditions and input-output examples with syntactic specifications in the form of term grammars and AST-level statistics extracted from code corpora. We show that synthesis in this framework can be viewed as an instance of graph search, permitting the use of well-understood families of techniques such as A*. We implement our algorithm in a framework for verification, synthesis and repair of functional programs, demonstrating that our approach can repair programs that are beyond the reach of previous tools.
Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This exponential gradient boosting is shown to speed up convergence and improve generalization. To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the latter case, using the state-of-the-art neural network architecture, the model converged 33% faster with our method (roughly two days of training less) than with the standard softmax activation, and with a slightly better performance to boot.
Multi-Antenna Assisted Virtual Full-Duplex Relaying with Reliability-Aware Iterative Decoding
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as transmitter and receiver to relay their decoded data signals regardless the decoding errors, meanwhile, cancel the inter-relay interference with QR-decomposition. Then, by deploying the reliability-aware iterative detection/decoding process, destination node can efficiently mitigate inter-frame interference and error propagation effect at the same time. Simulation results show that, without extra cost of time delay and signalling overhead, our proposed scheme outperforms the conventional selective decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms of bit-error-rate.